Practical Multi-Armed Bandit Algorithms in Python

Practical Multi-Armed Bandit Algorithms in Python, available at $69.99, has an average rating of 4.7, with 23 lectures, 3 quizzes, based on 140 reviews, and has 811 subscribers.

You will learn about Understanding and being able to identify Multi-Armed Bandit problems. Modelling real business problems as MAB and implementing digital AI agents to automate them. Understanding the challenge of RL regarding the exploration-exploitation dilema. Practical implementation of the various algorithmic strategies for balancing between exploration and exploitation. Python implementation of the Epsilon-greedy strategy. Python implementation of the Softmax Exploration strategy. Python implementation of the Optimistic Initialization strategy. Python implementation of the Upper Confidence Bounds (UCB) strategy. Understand the challenges of RL in terms of the design of reward functions and sample efficiency. Estimation of action values through incremental sampling. This course is ideal for individuals who are Anyone with a basic Python skills desiring to the started in Reinforcement Learning. or Experienced AI Engineers, ML Engineers, Data Scientist, and Software Engineers wanting to apply Reinforcement Learning to real business problems. or Business professionals willing to learn how Reinforcement Learning can help with automating adaptive decision making processes. It is particularly useful for Anyone with a basic Python skills desiring to the started in Reinforcement Learning. or Experienced AI Engineers, ML Engineers, Data Scientist, and Software Engineers wanting to apply Reinforcement Learning to real business problems. or Business professionals willing to learn how Reinforcement Learning can help with automating adaptive decision making processes.

Enroll now: Practical Multi-Armed Bandit Algorithms in Python

Summary

Title: Practical Multi-Armed Bandit Algorithms in Python

Price: $69.99

Average Rating: 4.7

Number of Lectures: 23

Number of Quizzes: 3

Number of Published Lectures: 23

Number of Published Quizzes: 3

Number of Curriculum Items: 26

Number of Published Curriculum Objects: 26

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

• Understanding and being able to identify Multi-Armed Bandit problems.
• Modelling real business problems as MAB and implementing digital AI agents to automate them.
• Understanding the challenge of RL regarding the exploration-exploitation dilema.
• Practical implementation of the various algorithmic strategies for balancing between exploration and exploitation.
• Python implementation of the Epsilon-greedy strategy.
• Python implementation of the Softmax Exploration strategy.
• Python implementation of the Optimistic Initialization strategy.
• Python implementation of the Upper Confidence Bounds (UCB) strategy.
• Understand the challenges of RL in terms of the design of reward functions and sample efficiency.
• Estimation of action values through incremental sampling.

Who Should Attend

• Anyone with a basic Python skills desiring to the started in Reinforcement Learning.
• Experienced AI Engineers, ML Engineers, Data Scientist, and Software Engineers wanting to apply Reinforcement Learning to real business problems.
• Business professionals willing to learn how Reinforcement Learning can help with automating adaptive decision making processes.

Target Audiences

• Anyone with a basic Python skills desiring to the started in Reinforcement Learning.
• Experienced AI Engineers, ML Engineers, Data Scientist, and Software Engineers wanting to apply Reinforcement Learning to real business problems.
• Business professionals willing to learn how Reinforcement Learning can help with automating adaptive decision making processes.